Ciência de Dados aplicada à Psicometria com R

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Bem vindo ao Curso!!

Aula 0 (17/08/2020) Introdução: data science, machine learning e deep learnig

• Slides: http://www.labape.com.br/rprimi/ds/t0_introducao.pptx

Aula 1 e 2 (24 e 31/08/2020) R, R-studio e análise exploratória gráfica com ggplot

• R e R-Studio

  • https://cran.r-project.org

  • https://rstudio.github.io/visual-markdown-editing/#/

• tidyverse

• R-markdown Slides: http://www.labape.com.br/rprimi/ds/garrett_grolemund_r_markdown.pdf

• R-markdown cheatsheet https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf

• ggplot https://rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf https://rstudio.cloud/learn/cheat-sheets

Aula 3-4 (14, 21 e 28/09/2020) Transformação de dados com dplyr e tidyr

• dplyr e tidyr

• dplyr cheatsheet https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf

• tidyr cheatsheet https://github.com/rstudio/cheatsheets/blob/master/data-import.pdf

Aula 5-6 (5/10, 12/10, 26/10, 9/11/2020) Programando em R com purrr

• Tipos de dados, programando em R, funções, controle e loops

• O pacote purrr

• List columns

• purrr cheatsheet https://github.com/rstudio/cheatsheets/blob/master/purrr.pdf

• Exemplo de simulação

  • Slides: Primi_IMPS_2018.pptx

  • Artigo: Primi, R. , Santos, D. , De Fruyt, F. and John, O. P. (2019), Comparison of classical and modern methods for measuring and correcting for acquiescence. Br J Math Stat Psychol. doi:10.1111/bmsp.12168

  • Códgos: https://github.com/rprimi/acqu_mirt

Aula 7 (16/11/2020) Aplicações com Natural Language Processig (NLP)

• Tidytext: https://www.tidytextmining.com/tidytext.html
• Uso do word-to-vec (word embeddings) para análise de textos
• Rosenbusch, H., Wanders, F., & Pit, I. L. (2020). The Semantic Scale Network: An online tool to detect semantic overlap of psychological scales and prevent scale redundancies. Psychological Methods, 25(3), 380-392. http://dx.doi.org.stanford.idm.oclc.org/10.1037/met0000244

Aula 8 Introdução ao Deep Learning com R

• Slides : http://www.labape.com.br/rprimi/ds/deep_learning.pptx